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Data science & machine learning reference

Data science,explained from the source.

Every concept explained from the book or paper it comes from, with a figure and a worked example that was actually run.

TryGradient descentLinear regressionBayes' theoremPerceptron

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Sigmoid Locked01 · Definition An S-shaped activation that squashes any real number into (0, 1) — the historical default for neural networks, now largely displaced from hidden layers because it strangles the gradient.σ(x)=11+e−x\sigma(x) = \frac{1}{1 + e^{-x}}02 · Intuition 6 sections · With access09 · Sources Dive into Deep LearningDeep Learning 2016

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Gradient descent

Dive into Deep LearningZhang, Lipton, Li, Smola

§12.4.1§12.5§12.3.1.1

Deep LearningGoodfellow, Bengio, Courville · 2016

§8.2.2§4.3.1

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Fig. 1 Gradient descent on a narrow valley — the path crosses the floor far faster than it runs along it.

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gradient_descent.pyOutput

$ python gradient_descent.pyeta=0.1   w=    2.7938  loss=0.0425eta=0.5   w=    3.0000  loss=0eta=0.9   w=    2.7938  loss=0.0425eta=1.05  w=   -6.4153  loss=88.65

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How it is made
Sources
  • Dive into Deep Learning
  • Deep Learning2016
  • Think Stats
  • Introduction to Information Retrieval2008
  • Deep Residual Learning for Image Recognition2015
  • An Image is Worth 16x16 Words2021
  • BERT2019
  • Denoising Diffusion Probabilistic Models2020
  • Language Models are Few-Shot Learners2020
  • Learning Transferable Visual Models From Natural Language Supervision2021
  • On Calibration of Modern Neural Networks2017
  • Sentence-BERT2019

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